Method and apparatus for providing natural language input in a cartographic system

ABSTRACT

An approach is provided for classifying natural language input into plurality of query processing levels. The approach involves parsing at least one natural language input into one or more words, wherein the at least one natural language input represents at least one cartographic query. The approach also involves mapping the one or more words to at least one function of a cartography system. The approach further involves determining an availability of the at least one function, one or more parameters associated with the at least one function, or a combination thereof with respect to at least one component of the cartography system. The approach also involves classifying the at least one cartographic query into at least one of a plurality of query processing levels based, at least in part, on the availability. The approach further involves initiating an execution of the at least one function at the classified at least one query processing level, wherein the execution of the at least one function generates one or more results of the at least one cartographic query based, at least in part, on the one or more parameters. The approach also involves causing, at least in part, a presentation of the one or more results as cartographic information in a user interface of the cartography system.

BACKGROUND

The interface between users and computerized system requires that theusers adapt to the capabilities of the computerized system for it tounderstand input information and requests. With the passage of time, andemergence of the computerized system's ability to perform NaturalLanguage Processing (NLP), the dynamics in interactions between usersand technology has evolved and has become more sophisticated. However,the system is inefficient and error prone and works best if their domainof usage is limited to a specific category. Then again, limited domainmay restrict interaction between the user and the system.

Some Example Embodiments

Therefore, there is a need for an approach for classifying naturallanguage input into plurality of query processing levels.

According to one embodiment, a method comprises parsing at least onenatural language input into one or more words, wherein the at least onenatural language input represents at least one cartographic query. Themethod also comprises mapping the one or more words to at least onefunction of a cartography system. The method further comprisesdetermining an availability of the at least one function, one or moreparameters associated with the at least one function, or a combinationthereof with respect to at least one component of the cartographysystem. The method also comprises classifying the at least onecartographic query into at least one of a plurality of query processinglevels based, at least in part, on the availability. The method furthercomprises initiating an execution of the at least one function at theclassified at least one query processing level, wherein the execution ofthe at least one function generates one or more results of the at leastone cartographic query based, at least in part, on the one or moreparameters. The method also comprises causing, at least in part, apresentation of the one or more results as cartographic information in auser interface of the cartography system.

According to another embodiment, an apparatus comprises at least oneprocessor, and at least one memory including computer program code forone or more computer programs, the at least one memory and the computerprogram code configured to, with the at least one processor, cause, atleast in part, the apparatus to parse at least one natural languageinput into one or more words, wherein the at least one natural languageinput represents at least one cartographic query. The apparatus is alsocaused to map the one or more words to at least one function of acartography system. The apparatus is further caused to determine anavailability of the at least one function, one or more parametersassociated with the at least one function, or a combination thereof withrespect to at least one component of the cartography system. Theapparatus is also caused to classify the at least one cartographic queryinto at least one of a plurality of query processing levels based, atleast in part, on the availability. The apparatus is further caused toinitiate an execution of the at least one function at the classified atleast one query processing level, wherein the execution of the at leastone function generates one or more results of the at least onecartographic query based, at least in part, on the one or moreparameters. The apparatus is also caused to cause, at least in part, apresentation of the one or more results as cartographic information in auser interface of the cartography system.

According to another embodiment, a computer-readable storage mediumcarries one or more sequences of one or more instructions which, whenexecuted by one or more processors, cause, at least in part, anapparatus to parse at least one natural language input into one or morewords, wherein the at least one natural language input represents atleast one cartographic query. The apparatus is also caused to map theone or more words to at least one function of a cartography system. Theapparatus is further caused to determine an availability of the at leastone function, one or more parameters associated with the at least onefunction, or a combination thereof with respect to at least onecomponent of the cartography system. The apparatus is also caused toclassify the at least one cartographic query into at least one of aplurality of query processing levels based, at least in part, on theavailability. The apparatus is further caused to initiate an executionof the at least one function at the classified at least one queryprocessing level, wherein the execution of the at least one functiongenerates one or more results of the at least one cartographic querybased, at least in part, on the one or more parameters. The apparatus isalso caused to cause, at least in part, a presentation of the one ormore results as cartographic information in a user interface of thecartography system.

According to another embodiment, an apparatus comprises means forparsing at least one natural language input into one or more words,wherein the at least one natural language input represents at least onecartographic query. The apparatus also comprises means for mapping theone or more words to at least one function of a cartography system. Theapparatus further comprises means for determining an availability of theat least one function, one or more parameters associated with the atleast one function, or a combination thereof with respect to at leastone component of the cartography system. The apparatus also comprisesmeans for classifying the at least one cartographic query into at leastone of a plurality of query processing levels based, at least in part,on the availability. The apparatus further comprises means forinitiating an execution of the at least one function at the classifiedat least one query processing level, wherein the execution of the atleast one function generates one or more results of the at least onecartographic query based, at least in part, on the one or moreparameters. The apparatus also comprises means for causing, at least inpart, a presentation of the one or more results as cartographicinformation in a user interface of the cartography system.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (or derived at least in part from)any one or any combination of methods (or processes) disclosed in thisapplication as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing the method of any oforiginally filed claims 1-10, 21-30, and 46-48.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings:

FIG. 1A is a diagram of a system capable of classifying natural languageinput into plurality of query processing levels, according to oneexample embodiment;

FIG. 1B is a diagram of the geographic database 111 of system 100,according to exemplary embodiments;

FIG. 2 is a diagram of the components of the computation platform 109,according to one example embodiment;

FIG. 3 is a flowchart of a process for classifying natural languageinput into plurality of query processing levels, according to oneexample embodiment;

FIG. 4 is a flowchart of a process for routing between at least twodetermined locations, according to one example embodiment;

FIG. 5 is a flowchart of a process for determining one or moreparameters and formatting one or more results, according to one exampleembodiment;

FIG. 6 is a flow diagram that represents the various query processinglevels for at least one natural language input, according to one exampleembodiment;

FIG. 7A is a user interface diagram that represents a scenario wherein anatural language input is interpreted to provide cartographicinformation in at least one user interface of a cartography system,according to one example embodiment;

FIG. 7B is a user interface diagram for interpreting the recognized textstring and deducing available functions required to serve the queryexpressed in the text string, according to one example embodiment;

FIG. 7C is a user interface diagram that represents contextual cachingand transition between plurality of query processing levels, accordingto one example embodiment;

FIG. 7D is a user interface diagram wherein at least one optimal routeis determined based, at least in part, on the natural language input,according to one example embodiment;

FIG. 8 is a diagram of hardware that can be used to implement anembodiment of the invention;

FIG. 9 is a diagram of a chip set that can be used to implement anembodiment of the invention; and

FIG. 10 is a diagram of a mobile terminal (e.g., handset) that can beused to implement an embodiment of the invention.

DESCRIPTION OF SOME EMBODIMENTS

Examples of a method, apparatus, and computer program for classifyingnatural language input into plurality of query processing levels aredisclosed. In the following description, for the purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the embodiments of the invention. It isapparent, however, to one skilled in the art that the embodiments of theinvention may be practiced without these specific details or with anequivalent arrangement. In other instances, well-known structures anddevices are shown in block diagram form in order to avoid unnecessarilyobscuring the embodiments of the invention.

FIG. 1A is a diagram of a system capable of classifying natural languageinput into plurality of query processing levels, according to oneembodiment. As discussed, users need to adapt to the capabilities of acomputerized system for the computerized system to comprehend theirrequests. Introducing NLP to cartography applications has significantlysimplified the interaction between users and their devices. Speech, asthe easiest form of input, is interpreted as a query that a cartographyapplication can handle. Though NLP services have alleviated theseprocesses, users owning mobile devices and using cartographyapplications are not trained to impart specific and unequivocalinstructions to their mobile devices. For example, user instructions canbe a compound sentence or fuzzy or incomplete. Since the system is stillerror prone, their domain of usage may be limited to a certain categoryto make the processing more accurate. However, such reduced informationset may limit the interaction between the users and their devices.

To address this problem, a system 100 of FIG. 1A introduces thecapability to classify natural language input into plurality of queryprocessing levels, according to one embodiment. The system 100 providesNLP service that allows the user to interact with the devices withouthaving to limit the user's verbiage to a limited set of directions byprocessing the natural language input in different levels. Therebyenabling users to utilize everyday speech to communicate with theirdevices. The system 100 performs compound sentence analysis with respectto a query in various query processing levels to achieve efficient mapsearch, high accuracy in map search results and fast delivery of mapsearch results to a user.

In one scenario, a natural language input is a language used by a humanuser (as opposed to a computer language or other artificial language),including all of the idioms, assumptions and implications of anutterance. The computation platform 109 may process the natural languageto determine the meaning of a natural language input such that thenatural language input can be “understood” and/or acted on by thesystem. In one scenario, the system 100 may parse the sentence in one ormore words to identify individual words in a natural language input. Inanother scenario, the system 100 may perform spell checking and/orgrammar checking for the at least one natural language input todetermine specific linguistic features.

Although various embodiments are described with respect to classifyingnatural language input into various processing levels, it iscontemplated that the approach described herein may be applicable to anyinvention that can be modelled according the example processes describedbelow and can benefit from classifying natural language input intovarious processing levels.

As shown in FIG. 1A, the system 100 comprises user equipment (UE) 101a-101 n (collectively referred to as UE 101) that may include or beassociated with applications 103 a-103 n (collectively referred to asapplications 103) and sensors 105 a-105 n (collectively referred to assensors 105). In one embodiment, the UE 101 has connectivity to acomputation platform 109 via a communication network 107, e.g., awireless communication network. In one embodiment, the computationplatform 109 performs one or more functions associated with classifyingnatural language input into plurality of query processing levels.

As shown in FIG. 1A, the system 100 comprises of UE 101. In oneembodiment, the UE 101 may include, but is not restricted to, any typeof a mobile terminal, wireless terminal, fixed terminal, or portableterminal. Examples of the UE 101, may include, but are not restrictedto, a mobile handset, a wireless communication device, a station, aunit, a device, a multimedia computer, a multimedia tablet, an Internetnode, a communicator, a desktop computer, a laptop computer, a notebookcomputer, a netbook computer, a tablet computer, a PersonalCommunication System (PCS) device, a personal navigation device, aPersonal Digital Assistant (PDA), a digital camera/camcorder, aninfotainment system, a dashboard computer, a television device, or anycombination thereof, including the accessories and peripherals of thesedevices, or any combination thereof. In one embodiment, the UE 101 maysupport any type of interface for supporting the presentment of one ormore recommended routes towards at least one destination. In addition,the UE 101 may facilitate various input means for receiving andgenerating information, including, but not restricted to, a voice-basedinput mechanism, touch screen capability, a keyboard and keypad dataentry, and the like. Any known and future implementations of the UE 101may also be applicable. In one embodiment, the UE 101 may be included,embedded within, or communicatively connected to the one or morevehicles (e.g., vehicle 129). In one embodiment, the at least one ormore vehicles 129 includes at least one autonomous vehicle, at least onehighly-assisted driving vehicle, or a combination thereof.

The UE 101 may further include applications 103. Further, theapplications 103 may include various applications such as, but notrestricted to, location-based service application, navigationapplication, content provisioning application, camera/imagingapplication, speech recognition application, voice to text converterapplication, media player application, social networking application,calendar applications, multimedia application, and the like. In oneembodiment, the applications 103 are installed within the UE 101. In oneexample embodiment, a voice-to-text converter application may convert ortranscribe a natural language input to a text message. In anotherexample embodiment, a location-based service application installed inthe UE 101 enables the computation platform 109 to determine, forexample, position, destination, heading, speed, context, identification,type, or any combination thereof, for at least one UE 101, such asvehicles.

The system 100 also includes one or more sensors 105, which can beimplemented, embedded or connected to the UE 101 and/or vehicle 129. Thesensors 105 may be any type of sensor. In certain embodiments, thesensors 105 may include, for example, but not restricted to, a globalpositioning sensor for gathering location data (e.g., Global NavigationSatellite System (GNSS) sensor), a network detection sensor fordetecting wireless signals or receivers for different short-rangecommunications (e.g., Bluetooth, Wi-Fi, Li-Fi, Near Field Communication(NFC) etc.), a camera/imaging sensor for gathering image data (e.g., thecamera sensors may automatically capture traffic flow information and/ortraffic light information), and the like. In one example embodiment, theUE 101 may include Global Positioning System (GPS) receivers to obtaingeographic coordinates from satellites 119 for determining currentlocation associated with the UE 101. Further, the location can bedetermined by an Assisted Global Positioning (A-GPS), Cell of Origin, awireless signal triangulation system, or other location extrapolationtechnologies. In another example embodiment, the sensors 105 may providein-vehicle navigation services, wherein one or more location basedservices may be provided to the at least one UE 101 associated with theat least one user of the vehicle and/or at least one other UE 101associated with the at least one vehicle. In a further exampleembodiment, the one or more sensors 105 in the UE 101 or vehicle 129enable determination, for example, position, destination, speed, typeand identification, or any combination thereof, for the UE 101 orvehicle 129. In another embodiment, the sensors 105 may enabledetermination regarding the status situation in one or more roadsegments, such as, traffic or weather. In another embodiment, thesensors 105 may enable determination of context of the UE 101 or vehicle129.

Further, various elements of the system 100 may communicate with eachother through a communication network 107. The communication network 107of system 100 includes one or more networks such as a data network, awireless network, a telephony network, or any combination thereof. It iscontemplated that the data network may be any local area network (LAN),metropolitan area network (MAN), wide area network (WAN), a public datanetwork (e.g., the Internet), short range wireless network, or any othersuitable packet-switched network, such as a commercially owned,proprietary packet-switched network, e.g., a proprietary cable orfiber-optic network, and the like, or any combination thereof. Inaddition, the wireless network may be, for example, a cellularcommunication network and may employ various technologies includingenhanced data rates for global evolution (EDGE), general packet radioservice (GPRS), global system for mobile communications (GSM), Internetprotocol multimedia subsystem (IMS), universal mobile telecommunicationssystem (UMTS), etc., as well as any other suitable wireless medium,e.g., worldwide interoperability for microwave access (WiMAX), Long TermEvolution (LTE) networks, code division multiple access (CDMA), widebandcode division multiple access (WCDMA), wireless fidelity (Wi-Fi),wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting,satellite, mobile ad-hoc network (MANET), vehicle controller areanetwork (CAN bus), and the like, or any combination thereof.

In one embodiment, the computation platform 109 may be a platform withmultiple interconnected components. The computation platform 109 mayinclude one or more servers, intelligent networking devices, computingdevices, components and corresponding software for classifying naturallanguage input into plurality of query processing levels. In addition,it is noted that the computation platform 109 may be a separate entityof the system 100, a part of the one or more services 115 a-115 n(collectively referred to as services 115) of the services platform 113,or the UE 101.

In one embodiment, the computation platform 109 implements plurality ofquery processing levels for dealing with queries relating to maps andnavigation. In one embodiment, the computation platform 109 may parse atleast one natural language input into one or more words. Then, thecomputation platform 109 may map the one or more parsed words to atleast one available function. Subsequently, the computation platform 109may classify the one or more words into one of the plurality of queryprocessing levels. In one example embodiment, the computation platform109 may receive at least one natural language input (e.g., “where is thegas station?”) from at least one user. Then, the computation platform109 may parse the sentence into one or more words for mapping with theat least one available functions. The trigger word “where” is associatedwith the display function and the trigger word “gas station” isassociated with the target object. Subsequently, the computationplatform 109 may classify the trigger words into various queryprocessing levels to get the results. In one scenario, a queryprocessing level may include a local and reduced mapping of textstrings, and may provide explicit instructions for a known query. Inanother scenario, a query processing level may include a formal searchin an external database because the knowledge required to satisfy thequery resides in a different database. In a further scenario, a queryprocessing level may include determining at least one route between thedevice's current location and a second location based on contextualinformation and/or location information. Subsequently, the computationplatform 109 causes a presentation of the result in a mapping userinterface.

In one embodiment, the geographic database 111 a-111 n (collectivelyreferred to as geographic database 111) may store one or more definedfunctions and/or one or more defined target objects. In one scenario,the one or more defined functions include records for natural languageinput created by collecting one or more input query sentences,extracting syntax from the one or more input query sentences, or acombination thereof. In another scenario, the one or more defined targetobjects includes an index that geographically correlates one or moretarget objects. In another embodiment, the geographic database 111 maystore one or more previous input queries, result for the one or moreprevious input queries, or a combination thereof. In one exampleembodiment, the geographic database 111 may include a local databasewith a reduced mapping of text strings and defined functions forresponding to the queries. The local database contains word-functionmappings collected during a training phase. In a further embodiment, thegeographic database 111 may store and manage data for one or more UE 101and/or one or more vehicles pertaining to queries. The information maybe any multiple types of information that can provide means for aidingin the content provisioning and sharing process. In one scenario, thegeographic database 111 may include lexicons. In another scenario, thegeographic database 111 can be updated by connecting to a backendservice.

The services platform 113 may include any type of service. By way ofexample, the services platform 113 may include mappingservices/application, navigation services/application, travel planningservices/application, route calculation services/application,notification services/application, social networkingservices/application, content (e.g., audio, video, images, etc.)provisioning services/application, application services/application,storage services/application, contextual information determinationservices/application, location based services/application, information(e.g., weather, news, etc.) based services/application, etc. In oneembodiment, the services platform 113 may interact with the UE 101, thecomputation platform 109 and the content provider 117 to supplement oraid in the processing of the content information. In one embodiment, theservices platform 113 may be implemented or embedded in the computationplatform 109 or in its functions.

By way of example, the services 115 may be an online service thatreflects interests and/or activities of users. The services 115 allowusers to share contact information, location information, activitiesinformation, contextual information, historical user information andinterests within their individual networks, and provides for dataportability. The services 115 may additionally assist in providing thecomputation platform 109 with information on travel plans of at leastone user, activity information of at least one user, user profileinformation, and a variety of additional information.

The content providers 117 a-117 n (collectively referred to as contentprovider 117) may provide content to the UE 101, the vehicle 129, thecomputation platform 109, and the services 115 of the services platform113. The content provided may be any type of content, such as, imagecontent, textual content, audio content (e.g., audio notification),video content (e.g., visual notification), etc. In one embodiment, thecontent provider 117 may provide content that may supplement content ofthe applications 103, the sensors 105, or a combination thereof. In oneembodiment, the content provider 117 may provide or supplement themapping services/application, navigation services/application, travelplanning services/application, route calculation services/application,notification services/application, social networkingservices/application, content (e.g., audio, video, images, etc.)provisioning services/application, application services/application,storage services/application, contextual information determinationservices/application, location based services/application, information(e.g., weather, news, etc.) based services/application, local map data,or any combination thereof. By way of example, the content provider 117may provide content that may aid in determining at least onecartographic query. In one embodiment, the content provider 117 may alsostore content associated with the UE 101, the computation platform 109,and the services 115 of the services platform 113. In anotherembodiment, the content provider 117 may manage access to a centralrepository of data, and offer a consistent, standard interface to data,such as, a repository of cartographic queries. Any known or stilldeveloping methods, techniques or processes for assigning at least onelocation to at least one contact may be employed by the computationplatform 109.

By way of example, the UE 101, the computation platform 109 communicatewith each other and other components of the communication network 107using well known, new or still developing protocols. In this context, aprotocol includes a set of rules defining how the network nodes withinthe communication network 107 interact with each other based oninformation sent over the communication links. The protocols areeffective at different layers of operation within each node, fromgenerating and receiving physical signals of various types, to selectinga link for transferring those signals, to the format of informationindicated by those signals, to identifying which software applicationexecuting on a computer system sends or receives the information. Theconceptually different layers of protocols for exchanging informationover a network are described in the Open Systems Interconnection (OSI)Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application (layer 5, layer 6 and layer 7) headersas defined by the OSI Reference Model.

FIG. 1B is a diagram of the geographic database 111 of system 100,according to exemplary embodiments. In the exemplary embodiments, POIsand map generated POIs data can be stored, associated with, and/orlinked to the geographic database 111 or data thereof. In oneembodiment, the geographic or map database 111 includes geographic data121 used for (or configured to be compiled to be used for) mappingand/or navigation-related services, such as for personalized routedetermination, according to exemplary embodiments. For example, thegeographic database 111 includes node data records 123, road segment orlink data records 125, POI data records 127, and other data records 131,for example. More, fewer or different data records can be provided. Inone embodiment, the other data records 131 include cartographic(“carto”) data records, routing data, traffic signal data, and maneuverdata. One or more portions, components, areas, layers, features, text,and/or symbols of the POI or event data can be stored in, linked to,and/or associated with one or more of these data records. For example,one or more portions of the POI, event data, or recorded routeinformation can be matched with respective map or geographic records viaposition or GPS data associations (such as using known or future mapmatching or geo-coding techniques), for example.

In exemplary embodiments, the road segment data records 125 are links orsegments representing roads, streets, parking areas, or paths, as can beused in the calculated route or recorded route information fordetermination of one or more personalized routes, according to exemplaryembodiments. The node data records 123 are end points corresponding tothe respective links or segments of the road segment data records 125.The road link data records 125 and the node data records 123 represent aroad network, such as used by vehicles, cars, and/or other entities.Alternatively, the geographic database 111 can contain path segment andnode data records or other data that represent pedestrian paths or areasin addition to or instead of the vehicle road record data, for example.

The road link and nodes can be associated with attributes, such asgeographic coordinates, traffic lights, street names, address ranges,speed limits, turn restrictions at intersections, and other navigationrelated attributes, as well as POIs, such as gasoline stations, hotels,restaurants, museums, stadiums, offices, automobile dealerships, autorepair shops, buildings, stores, parks, parking areas (attributes onwhich parking areas are critical) etc. The geographic database 111 caninclude data about the POIs and their respective locations in the POIdata records 127. The geographic database 111 can also include dataabout places, such as cities, towns, or other communities, and othergeographic features, such as bodies of water, mountain ranges, etc. Suchplace or feature data can be part of the POI data 127 or can beassociated with POIs or POI data records 127 (such as a data point usedfor displaying or representing a position of a city). In addition, thegeographic database 111 can include data from radio advertisementsassociated with the POI data records 127 and their respective locationsin the radio generated POI records 129. By way of example, a street isdetermined from the user interaction with the UE 101 and the contentinformation associated with the UE 101, according to the variousembodiments described herein.

The geographic database 111 can be maintained by the content provider inassociation with the services platform 113 (e.g., a map developer). Themap developer can collect geographic data to generate and enhance thegeographic database 111. There can be different ways used by the mapdeveloper to collect data. These ways can include obtaining data fromother sources, such as municipalities or respective geographicauthorities (e.g., schedules for traffic light signals). In addition,the map developer can employ field personnel to travel by vehicle alongroads throughout the geographic region to observe features and/or recordinformation about them, for example. Also, remote sensing, such asaerial or satellite photography, can be used.

The geographic database 111 can be a master geographic database storedin a format that facilitates updating, maintenance, and development. Forexample, the master geographic database 111 or data in the mastergeographic database 111 can be in an Oracle spatial format or otherspatial format, such as for development or production purposes. TheOracle spatial format or development/production database can be compiledinto a delivery format, such as a geographic data files (GDF) format.The data in the production and/or delivery formats can be compiled orfurther compiled to form geographic database products or databases,which can be used in end user navigation devices or systems.

For example, geographic data is compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by a UE 101, for example. The navigation-relatedfunctions can correspond to vehicle navigation, pedestrian navigation,or other types of navigation. The compilation to produce the end userdatabases can be performed by a party or entity separate from the mapdeveloper. For example, a customer of the map developer, such as anavigation device developer or other end user device developer, canperform compilation on a received geographic database in a deliveryformat to produce one or more compiled navigation databases.

As mentioned above, the geographic database 111 can be a mastergeographic database, but in alternate embodiments, the geographicdatabase 111 can represent a compiled navigation database that can beused in or with end user devices (e.g., UEs 101) to providednavigation-related functions. For example, the geographic database 111can be used with the UE 101 to provide an end user with navigationfeatures. In such a case, the geographic database 111 can be downloadedor stored on the UE 101, such as in the applications 103, or the UE 101can access the geographic database 111 through a wireless or wiredconnection (such as via a server and/or the communication network 107),for example.

In one embodiment, the end user device or UE 101 can be an in-vehiclenavigation system, a personal navigation device (PND), a portablenavigation device, a cellular telephone, a mobile phone, a personaldigital assistant (PDA), a watch, a camera, a computer, and/or otherdevice that can perform navigation-related functions, such as digitalrouting and map display. In one embodiment, the navigation device UE 101can be a cellular telephone. An end user can use the device UE 101 fornavigation functions, for example, map display for travelling in a routetowards at least one destination.

FIG. 2 is a diagram of the components of the computation platform 109,according to one example embodiment. By way of example, the computationplatform 109 may include one or more components for classifying naturallanguage input into plurality of query processing levels. In oneembodiment, the computation platform 109 may include parsing modules201, mapping module 203, classification modules 205, inquiry modules207, monitoring module 209, logic modules 211, communication modules213, and presentation modules 215. It is contemplated that the functionsof these components may be combined in one or more components orperformed by other components with similar functionalities.

In one embodiment, the parsing module 201 may parse at least one naturallanguage input into one or more words. In one scenario, the parsingmodule 201 may extract syntactic and semantic information for the one ormore words to determine complete phrases, partial phrases, or keywords.In another scenario, at least one natural language input includes atleast one specialized natural language input, at least one formal searchnatural language input, at least one ambiguous natural language input,or a combination thereof.

In one embodiment, the mapping module 203 may map the one or more parsedwords to at least one function from a set of functions in at least onequery processing level. In one scenario, the mapping module 203 maycause a language mapping, a synonym mapping, a geospatial mapping, or acombination thereof for the at least one natural language input. Inanother scenario, a synonym mapping includes mapping synonymous words,abbreviated words, or a combination thereof. In a further scenario, themapping module 203 may determine search parameters to limit the searchin the at least one query processing level.

In one embodiment, the classification module 205 may categorize one ormore topics of interest to one or more appropriate query processinglevel. In another embodiment, the classification module 205 may assistin routing the natural language input to one or more appropriate queryprocessing level.

In one embodiment, the inquiry module 207 may determine a meaning forthe at least one ambiguous natural language input by using input queryhistory of the at least one user. In one scenario, the at least oneambiguous input query includes at least one incomplete input query. Inanother embodiment, the inquiry module 207 may determine one or moretrigger words from the at least one natural language input, wherein theone or more trigger words causes a filtering of the at least onefunction. In a further embodiment, the inquiry module 207 may determinesimilarity among at least one input query, at least one previous inputquery, or a combination thereof. Then, the inquiry module 207 may cause,at least in part, a selection of the at least one previous input querybased, at least in part, on the similarity, temporal information of theat least one previous input query, placement of the at least oneprevious input query, or a combination thereof.

In one embodiment, the monitoring module 209 may monitor at least onefunction, at least one target object, or a combination thereof inreal-time. The monitoring module 209 may determine unavailability of theat least one function, at least one target object, or a combinationthereof in the at least one query processing level, whereupon themonitoring module 209 may search for at least one function, at least onetarget object, or a combination thereof in other query processinglevels. In one scenario, at least one target object includes an indexthat geographically correlates to at least one point of interest.

In one embodiment, the logic module 211 may manage tasks, includingtasks performed by the other modules. For example, although the othermodules may perform their actual tasks, the logic module 211 maydetermine when and how those tasks are performed or otherwise direct theother modules to perform the task.

The communication module 213 may be used for communication betweenvarious elements of the system 100 as well as between modules, elements,components, etc. of the computation platform 109. For example, thecommunication module 213 may be used to communicate commands, requests,etc., to/from the UE 101, computation platform 109, geographic database111, the content provider 117, or the like.

In one embodiment, the presentation module 215 obtains a set of summarystatistics and/or updates from the other modules. In one exampleembodiment, the presentation module 215 continues with generating anaudio and/or video presentation of mapping and navigation information ina user interface of at least one device based, at least in part, oncontextual information associated with at least one device associatedwith the at least one user, location information associated with the oneor more target objects, or a combination thereof.

The above presented modules and components of the computation platform109 can be implemented in hardware, firmware, software, or a combinationthereof. Though depicted as a separate entity in FIG. 1A, it iscontemplated that the computation platform 109 may be implemented fordirect operation by respective UE 101. As such, the computation platform109 may generate direct signal inputs by way of the operating system ofthe UE 101 for interacting with the applications 103. In anotherembodiment, one or more of the modules 201-215 may be implemented foroperation by respective UEs, as the computation platform 109, orcombination thereof. Still further, the computation platform 109 may beintegrated for direct operation with the services 115, such as in theform of a widget or applet, in accordance with an information and/orsubscriber sharing arrangement. The various executions presented hereincontemplate any and all arrangements and models.

FIG. 3 is a flowchart of a process for classifying natural languageinput into plurality of query processing levels, according to oneexample embodiment. In one embodiment, the computation platform 109performs the process 300 and is implemented in, for instance, a chip setincluding a processor and a memory as shown in FIG. 9.

In step 301, the computation platform 109 may parse at least one naturallanguage input into one or more words. In one embodiment, the at leastone natural input represents at least one cartographic query. In oneexample embodiment, the computation platform 109 may recognize asequence of characters, letters, words or phrases representing addressrelated information from a natural language input during parsing. Thisrecognition of address related information can be achieved by, forexample, searching in one or more database of the geographic databases111.

In step 303, the computation platform 109 may map the one or more wordsto at least one function of a cartography system. In one scenario, thecomputation platform 109 may access geographic database 111 to cause amapping of the one or more words to at least one defined function, atleast one defined target object, or a combination thereof.

In step 305, the computation platform 109 may determine an availabilityof the at least one function, one or more parameters associated with theat least one function, or a combination thereof with respect to at leastone component of the cartography system. In one embodiment, thecomputation platform 109 may determine the availability of at least onefunction on receiving the at least one natural language input by the atleast one component of the cartography system.

In step 307, the computation platform 109 may classify the at least onecartographic query into at least one of a plurality of query processinglevels based, at least in part, on the availability. In one embodiment,the computation platform 109 may classify the at least one cartographicquery on receiving the at least one natural language input by the atleast one component of the cartography system. In one embodiment, theplurality of the query processing levels include, at least in part, afirst processing level at which the at least one function, the one ormore parameters, or a combination thereof are executed in whole locallyat the at least one component of the cartography system. In anotherembodiment, the plurality of the query processing levels include, atleast in part, a second processing level at which the at least onefunction, the one or more parameters, or a combination thereof areexecuted, at least in part, by conducting at least one search externalto the at least component of the cartography system. In a furtherembodiment, the plurality of the query processing levels include, atleast in part, a third processing level at which the at least onefunction, the one or more parameters, or a combination thereof areexecuted, at least in part, by routing between at least two locations.

In step 309, the computation platform 109 may initiate an execution ofthe at least one function at the classified at least one queryprocessing level. The execution of the at least one function generatesone or more results of the at least one cartographic query based, atleast in part, on the one or more parameters. In one embodiment, thecomputation platform 109 may execute at least one function uponreceiving the at least one natural language input by the at least onecomponent of the cartography system. In one scenario, the computationplatform 109 may deduce the function and the arguments from the naturallanguage input. Then, the computation platform 109 may check for thepresence of the arguments at the moment of the request in firstprocessing level. If any of the arguments are not present, the search orrouting is initiated in the second processing level or third processinglevel, depending on the intended function. In one example embodiment,the computation platform 109 may determine that first processing leveldoes not have the list of target objects based on specified criteria(e.g., proximity information, location information, etc.). Then, thecomputation platform 109 may initiate a new search in a differentprocessing level.

In step 311, the computation platform 109 may cause, at least in part, apresentation of the one or more results as cartographic information in auser interface of the cartography system. In one example embodiment, thecomputation platform 109 may present a map with determined routeshighlighted therein towards one or more POIs.

FIG. 4 is a flowchart of a process for routing between at least twodetermined locations, according to one example embodiment. In oneembodiment, the computation platform 109 performs the process 400 and isimplemented in, for instance, a chip set including a processor and amemory as shown in FIG. 9.

In step 401, the computation platform 109 may determine the at least twolocations based, at least in part, on the at least one function, the oneor more parameters, contextual information, or a combination thereof. Inone scenario, two or more objects on a map may have different locations.The computation platform 109 may receive contextual information for atleast one UE 101 associated with at least one user and the locationinformation (or contextual information) for one or more POIs. Then, thecomputation platform 109 may determine at least one route between thecurrent location of at least one UE 101 and a second location.

In step 403, the computation platform 109 may determine the routingbetween the at least two locations without regard to an input order ofat least one location. In one example embodiment, several target objectswith different location and/or contexts may be present in a singleinstructions (or series of instructions). The computation platform 109may classify such instructions to third processing level to determine anoptimal way of planning at least one route irrespective of the explicitorder expressed in the instructions. In one scenario, the thirdprocessing level may determine whether a route optimization can takeplace and automatically request it from the routing API when submittinga request. In one example embodiment, an optimal method of planning atleast one route may include integration of calendar information of atleast one user (e.g., meetings/appointments in a user's calendar). Inone scenario, the calendar information may comprise of locationinformation for the scheduled meeting, date information for thescheduled meeting, time information for the scheduled meeting, purposeof the scheduled meeting etc. In one embodiment, the computationplatform 109 may query at least one database associated with the atleast one user to determine at least one calendar event. The calendarevent may be shared by the computation platform 109 with at least onenavigation system. Then, the routing algorithm may take the input fromthe NLP processing, and check for locations and timings of the calendarevent(s). Subsequently, the routing algorithm determines a moreefficient route that is compatible with the NLP input. In oneembodiment, the one or more routes may indicate, at least in part, adirection, a pace, or a combination thereof based, at least in part, onthe calendar information.

FIG. 5 is a flowchart of a process for determining one or moreparameters and formatting one or more results, according to one exampleembodiment. In one embodiment, the computation platform 109 performs theprocess 500 and is implemented in, for instance, a chip set including aprocessor and a memory as shown in FIG. 9.

In step 501, the computation platform 109 may determine one or moreparameters from a natural language input. In one example embodiment, auser may provide an input query (e.g., “Where can I find banks,excluding ABC banks?”). Then, the computation platform 109 may use thetrigger word “where” as a search parameter to find a target (i.e.,banks). In one scenario, trigger word “where” reduces the search area ascompared to “close to” and/or “in”. In addition, trigger word“excluding” is used as a parameter to filter POIs that are of nointerest to the user (e.g., ABC banks). Subsequently, the computationplatform 109 may display nearby banks apart from ABC banks in a userinterface of a cartography application.

In step 503, the computation platform 109 may determine the one or moreparameters from at least one contextual cache. In one embodiment, the atleast one contextual cache stores one or more previous results, one ormore previous functions, one or more previous parameters, or acombination thereof associated with at least one previous cartographicquery. In one scenario, a command issued in a natural language may oftenbe contextual. The computation platform 109 may retain previouslyreceived statements (e.g., queries or instructions) and the answers tothe statements. In one example embodiment, a user may want to refine apreviously issued search, or request for navigation alternatives, orrequest additional information about a POI, and so on. In doing so, thepreviously received statements and previously provided results are usedto fill in the unknown arguments in the search. For example, if aninitial query is “where is the next coffee shop” and the nextinstruction is “take me there”, the level 1 (L1) processing needs to beable to retrieve the result of the preceding query to issue theinstruction “FIND_ROUTE to <coffee shop location>.” In one scenario, innatural language, the immediately previous query/reply will be the mostrelevant for filling out the unknown arguments. The relevance ofprevious queries/replies for ambiguous arguments decreases as theprevious queries become older in time or as they move down in theirorder. In one example embodiment, the last result might not be asrelevant for finding a route if the search location was performed hoursago (rather than within 5 minutes) or if there was another query issuedin between. The following are examples of how caching enablescomputation platform 109 to understand conversation context:

-   -   1. “Show me closest the Starbucks?”    -   2. “Take me there” or “Drive me there” or “Walk me there”    -   3. “How far is it?” or “What is the address?”        Asking for address or other POI info depends on the previously        mentioned contextual information.

In step 505, the computation platform 109 may format the one or moreresults according to at least one format associated with at least onemodule external to the at least one component. In one exampleembodiment, information stored in one or more databases are in an easilyaccessible JSON based format, although other formats may also besuitable. In one scenario, the three query processing levels may resultin providing a specific formatted output that can be utilized by anexternal module, be it a Text-to-speech (TTS) module for responding, asearch API, routing API, etc.

In step 507, the computation platform 109 may transmit the formatted oneor more results to the at least one module to initiate the presentationof the one or more results in the user interface. In one scenario, thecomputation platform 109 may forward the formatted result to a differentreceiving API based, at least in part, on the query processing levelthat is processing the incoming string.

FIG. 6 is a flow diagram that represents the various query processinglevels for at least one natural language input, according to one exampleembodiment. In one example embodiment, one or more users may avoid usingthe screen of the phone to find at least one location by using his/hervoice. In step 601, the voice is captured by the voice recognitionservices in at least one UE 101, and transformed into texts. In step603, the resulting texts can be fed into a natural language processing(NLP) module as a text string. The NLP module formalizes the request anddeduces the semantics of the sentences. The NLP module is tasked withinterpreting the recognized text string and deducing which of theavailable functions may be required to serve the query expressed in thestring. In order to correctly handle the many possible queries, the textprocessing needs to be handled on three different levels, which aretuned for the specific requirements of a cartography application. Instep 605, the function handler module may classify the different wordsin the received string. In natural language, words can indicatedifferent functions, relationships, objects, etc. For example, a usermay state “show me hotels,” the function is to display something and theparameter is the target object (i.e., hotels). In step 607, based on theavailable functions (e.g., F (x,y,z) parameters) command execution mayhappen on level 1 (L1), level 2 (L2) or level 3 (L3). In one scenario,processing level at L1 relates to explicit instructions, which are wellknown by the NLP module. L1 represents a level of ‘a priori’ knowledge,a local and reduced mapping of text strings and defined functions forresponding. These can be thought of as direct function calls. A localdatabase of NLP Knowledge may contain all of these word-functionmappings that have been collected during a training phase. These arestored in an easily accessible JSON based format, although other formatsmay also be suitable. In L1 the system provides an immediate reactionwhen queried information is available. In another scenario, L2 refers toa level of processing where the knowledge required to satisfy the queryresides outside of the NLP module. As such, L2 processing builds aformal search query that can be submitted to a database external to theNLP module, be it a local database (e.g. POI repository) or an onlinesearch engine. This level of processing serves for determining thelocation of different POIs. In a further scenario, the L3 refers to alevel of processing in which queries/instructions may be more complex,involving several physical objects. As two or more objects on a map tendto have different locations, routes can be determined between thedevice's current location and a second location. This level ofprocessing involves submitting information about the user's context andthe POIs location (or even context). In addition, when several targetobjects with different location/contexts are present in a single command(or series of commands), L3 may identify that the situation may involvean optimal way of planning the route, other than the explicit orderexpressed in the command. In other words, L3 may determine whether aroute optimization can take place and automatically request it from therouting API when submitting a request. Subsequently, the result may bepresented as cartographic information in a user interface of thecartography system [609]. In one embodiment, all three levels result inproviding a specific formatted output that can be utilized by anexternal module, be it a Text-to-speech (TTS) module for responding, asearch API, routing API, etc. Depending on which level processes theincoming string, a response may be forwarded to a different receivingAPI.

FIG. 7A is a user interface diagram that represents a scenario wherein anatural language input is interpreted to provide cartographicinformation in at least one user interface of a cartography system,according to one example embodiment. In one scenario, user A may state“Where can I find a place to eat?” to his/her UE 101. Then, thecomputation platform 109 may parse the natural language input, and mayassociate the trigger word “where” with the function “SHOW_ON_MAP”.Further, the computation platform 109 may consider the set of words“places to eat” as a synonym for restaurants, which leads to the querytarget object “restaurants”. In one example embodiment, the computationplatform 109 may parse and map user A's statement “Where can I find aplace to eat?” as follows:

-   -   “Where” Function=SHOW_ON_MAP    -   “Places to eat” Target=restaurants.    -   That deduced function is ‘search’ with the argument=restaurants.

Then, the cartography application in at least one UE 101 may reply to asearch function by displaying a list of restaurant with locationinformation in the at least one mapping interface UE 101 [701].

FIG. 7B is a user interface diagram for interpreting the recognized textstring and deducing available functions required to serve the queryexpressed in the text string, according to one example embodiment. Inone scenario, user A may state “Find me hotels close to space needle inSeattle excluding motels.” The statement shows a more specialized query,wherein arguments such as “where” and “near”, which were deduced from“close to” and “in”, are used as search parameters that reduce thesearch area. In addition, words such as “excluding” or “but not” areused to filter point of interest (POIs) which are of no interest to theuser. In one embodiment, the computation platform 109 may train themapping module 203 to associate words in a string with the relevantclassification. In one scenario, the computation platform 109 may parseand map user A's statement “Find me hotels close to space needle inSeattle excluding motels” as follows:

-   -   Function=SHOW_ON_MAP    -   Where=Seattle    -   Near=space needle    -   Target=hotels    -   Exception=motels

Then, the cartography application in at least one UE 101 may reply to asearch function by displaying a list of hotels close to space needle[703] in Seattle in the at least one mapping interface UE 101 [705]. Thesearch excludes any motels within the geographic area. In anotherscenario, the computation platform 109 may associate ambiguous wordswith their cartography-related meanings rather than with their alternatemeanings.

FIG. 7C is a user interface diagram that represents contextual cachingand transition between plurality of query processing levels, accordingto one example embodiment. In one scenario, the first query processinglevel may relate to explicit instructions, and the knowledge required tosatisfy the query resides within database 111. In another scenario, thesecond query processing level may relate to a level of processing wherethe knowledge required to satisfy the query resides outside the database111. In a further scenario, the third query processing level may includerouting between at least two locations based on at least one functionand/or at least one parameter and/or contextual information. In oneembodiment, the computation platform 109 may execute at least onecommand between the plurality of levels based on the availability of theone or more parameters (e.g., F(x,y,z)). In one example embodiment, userA may state “Where is the closest coffee shop”, whereupon thecomputation platform 109 may parse and map user A's statement. Thecomputation platform 109 may cause first query processing level andsecond query processing level to determine nearby coffee shops. Once thenearby coffee shops are determined, the computation platform 109 maycause the third query processing level to calculate routes to theclosest coffee shops, whereupon the route to the closest coffee shop isdisplayed to the user. In another example embodiment, user A may state“Take me there”, whereupon the computation platform 109 may parse andmap the natural language input to cause contextual caching.Subsequently, the computation platform 109 may start the navigation atlevel 1. The resolved target object, here <location of the closestcoffee shop>, is the answer taken from the contextual cache, as it waspreviously provided as a result for the target=<closest coffee shop>.Then, the cartography application in at least one UE 101 may reply tothe natural language input by displaying a route from the user's currentlocation [707] to the closest coffee shop [709] in the at least onemapping interface UE 101 [711].

FIG. 7D is a user interface diagram wherein at least one optimal routeis determined based, at least in part, on the natural language input,according to one example embodiment. In one scenario, user A may utter“take me to ABC city and stop at a restaurant” to his/her UE 101.Subsequently, user A may also utter “also stop at a departmental store.”Since the command involves multiple target destinations, the computationplatform 109 may come up with a more optimal way to build a route ratherthan follow the exact order specified by user A. The computationplatform 109 may determine at least one route based, at least in part,on contextual information associated with at least one device associatedwith user A, location information of the at least one restaurant,location information of the at least one department store, or acombination thereof. Then, the computation platform 109 may cause apresentation of at least one route that includes at least one restaurant[713], at least one department store [715] within ABC city. In anotherscenario, the computation platform 109 may incorporate distanceinformation (e.g., the closest restaurant and department store), trafficdensity information (e.g., the fastest route to the closest restaurantand department store), temporal information (e.g., the timing of therestaurant and department store), or a combination thereof whiledetermining at least one optimal route.

The processes described herein for classifying natural language inputinto plurality of query processing levels may be advantageouslyimplemented via software, hardware, firmware or a combination ofsoftware and/or firmware and/or hardware. For example, the processesdescribed herein, may be advantageously implemented via processor(s),Digital Signal Processing (DSP) chip, an Application Specific IntegratedCircuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc. Suchexemplary hardware for performing the described functions is detailedbelow.

FIG. 8 illustrates a computer system 800 upon which an embodiment of theinvention may be implemented. Although computer system 800 is depictedwith respect to a particular device or equipment, it is contemplatedthat other devices or equipment (e.g., network elements, servers, etc.)within FIG. 8 can deploy the illustrated hardware and components ofsystem 800. Computer system 800 is programmed (e.g., via computerprogram code or instructions) to classify natural language input intoplurality of query processing levels as described herein and includes acommunication mechanism such as a bus 810 for passing informationbetween other internal and external components of the computer system800. Information (also called data) is represented as a physicalexpression of a measurable phenomenon, typically electric voltages, butincluding, in other embodiments, such phenomena as magnetic,electromagnetic, pressure, chemical, biological, molecular, atomic,sub-atomic and quantum interactions. For example, north and southmagnetic fields, or a zero and non-zero electric voltage, represent twostates (0, 1) of a binary digit (bit). Other phenomena can representdigits of a higher base. A superposition of multiple simultaneousquantum states before measurement represents a quantum bit (qubit). Asequence of one or more digits constitutes digital data that is used torepresent a number or code for a character. In some embodiments,information called analog data is represented by a near continuum ofmeasurable values within a particular range. Computer system 800, or aportion thereof, constitutes a means for performing one or more steps ofclassifying natural language input into plurality of query processinglevels.

A bus 810 includes one or more parallel conductors of information sothat information is transferred quickly among devices coupled to the bus810. One or more processors 802 for processing information are coupledwith the bus 810.

A processor (or multiple processors) 802 performs a set of operations oninformation as specified by computer program code related to classifyingnatural language input into plurality of query processing levels. Thecomputer program code is a set of instructions or statements providinginstructions for the operation of the processor and/or the computersystem to perform specified functions. The code, for example, may bewritten in a computer programming language that is compiled into anative instruction set of the processor. The code may also be writtendirectly using the native instruction set (e.g., machine language). Theset of operations include bringing information in from the bus 810 andplacing information on the bus 810. The set of operations also typicallyinclude comparing two or more units of information, shifting positionsof units of information, and combining two or more units of information,such as by addition or multiplication or logical operations like OR,exclusive OR (XOR), and AND. Each operation of the set of operationsthat can be performed by the processor is represented to the processorby information called instructions, such as an operation code of one ormore digits. A sequence of operations to be executed by the processor802, such as a sequence of operation codes, constitute processorinstructions, also called computer system instructions or, simply,computer instructions. Processors may be implemented as mechanical,electrical, magnetic, optical, chemical, or quantum components, amongothers, alone or in combination.

Computer system 800 also includes a memory 804 coupled to bus 810. Thememory 804, such as a random access memory (RAM) or any other dynamicstorage device, stores information including processor instructions forclassifying natural language input into plurality of query processinglevels. Dynamic memory allows information stored therein to be changedby the computer system 800. RAM allows a unit of information stored at alocation called a memory address to be stored and retrievedindependently of information at neighboring addresses. The memory 804 isalso used by the processor 802 to store temporary values duringexecution of processor instructions. The computer system 800 alsoincludes a read only memory (ROM) 806 or any other static storage devicecoupled to the bus 810 for storing static information, includinginstructions, that is not changed by the computer system 800. Somememory is composed of volatile storage that loses the information storedthereon when power is lost. Also coupled to bus 810 is a non-volatile(persistent) storage device 808, such as a magnetic disk, optical diskor flash card, for storing information, including instructions, thatpersists even when the computer system 800 is turned off or otherwiseloses power.

Information, including instructions for classifying natural languageinput into plurality of query processing levels, is provided to the bus810 for use by the processor from an external input device 812, such asa keyboard containing alphanumeric keys operated by a user, amicrophone, an Infrared (IR) remote control, a joystick, a game pad, astylus pen, a touch screen, or a sensor. A sensor detects conditions inits vicinity and transforms those detections into physical expressioncompatible with the measurable phenomenon used to represent informationin computer system 800. Other external devices coupled to bus 810, usedprimarily for interacting with humans, include a display device 814,such as a cathode ray tube (CRT), a liquid crystal display (LCD), alight emitting diode (LED) display, an organic LED (OLED) display, aplasma screen, or a printer for presenting text or images, and apointing device 816, such as a mouse, a trackball, cursor directionkeys, or a motion sensor, for controlling a position of a small cursorimage presented on the display 814 and issuing commands associated withgraphical elements presented on the display 814, and one or more camerasensors 894 for capturing, recording and causing to store one or morestill and/or moving images (e.g., videos, movies, etc.) which also maycomprise audio recordings. In some embodiments, for example, inembodiments in which the computer system 800 performs all functionsautomatically without human input, one or more of external input device812, display device 814 and pointing device 816 may be omitted.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (ASIC) 820, is coupled to bus810. The special purpose hardware is configured to perform operationsnot performed by processor 802 quickly enough for special purposes.Examples of ASICs include graphics accelerator cards for generatingimages for display 814, cryptographic boards for encrypting anddecrypting messages sent over a network, speech recognition, andinterfaces to special external devices, such as robotic arms and medicalscanning equipment that repeatedly perform some complex sequence ofoperations that are more efficiently implemented in hardware.

Computer system 800 also includes one or more instances of acommunications interface 870 coupled to bus 810. Communication interface870 provides a one-way or two-way communication coupling to a variety ofexternal devices that operate with their own processors, such asprinters, scanners and external disks. In general the coupling is with anetwork link 878 that is connected to a local network 880 to which avariety of external devices with their own processors are connected. Forexample, communication interface 870 may be a parallel port or a serialport or a universal serial bus (USB) port on a personal computer. Insome embodiments, communications interface 870 is an integrated servicesdigital network (ISDN) card or a digital subscriber line (DSL) card or atelephone modem that provides an information communication connection toa corresponding type of telephone line. In some embodiments, acommunication interface 870 is a cable modem that converts signals onbus 810 into signals for a communication connection over a coaxial cableor into optical signals for a communication connection over a fiberoptic cable. As another example, communications interface 870 may be alocal area network (LAN) card to provide a data communication connectionto a compatible LAN, such as Ethernet. Wireless links may also beimplemented. For wireless links, the communications interface 870 sendsor receives or both sends and receives electrical, acoustic orelectromagnetic signals, including infrared and optical signals, thatcarry information streams, such as digital data. For example, inwireless handheld devices, such as mobile telephones like cell phones,the communications interface 870 includes a radio band electromagnetictransmitter and receiver called a radio transceiver. In certainembodiments, the communications interface 870 enables connection to thecommunication network 107 for classifying natural language input intoplurality of query processing levels to the UE 101.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing information to processor 802, includinginstructions for execution. Such a medium may take many forms,including, but not limited to computer-readable storage medium (e.g.,non-volatile media, volatile media), and transmission media.Non-transitory media, such as non-volatile media, include, for example,optical or magnetic disks, such as storage device 808. Volatile mediainclude, for example, dynamic memory 804. Transmission media include,for example, twisted pair cables, coaxial cables, copper wire, fiberoptic cables, and carrier waves that travel through space without wiresor cables, such as acoustic waves and electromagnetic waves, includingradio, optical and infrared waves. Signals include man-made transientvariations in amplitude, frequency, phase, polarization or otherphysical properties transmitted through the transmission media. Commonforms of computer-readable media include, for example, a floppy disk, aflexible disk, hard disk, magnetic tape, any other magnetic medium, aCD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape,optical mark sheets, any other physical medium with patterns of holes orother optically recognizable indicia, a RAM, a PROM, an EPROM, aFLASH-EPROM, an EEPROM, a flash memory, any other memory chip orcartridge, a carrier wave, or any other medium from which a computer canread. The term computer-readable storage medium is used herein to referto any computer-readable medium except transmission media.

Logic encoded in one or more tangible media includes one or both ofprocessor instructions on a computer-readable storage media and specialpurpose hardware, such as ASIC 820.

Network link 878 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 878 mayprovide a connection through local network 880 to a host computer 882 orto equipment 884 operated by an Internet Service Provider (ISP). ISPequipment 884 in turn provides data communication services through thepublic, world-wide packet-switching communication network of networksnow commonly referred to as the Internet 890.

A computer called a server host 892 connected to the Internet hosts aprocess that provides a service in response to information received overthe Internet. For example, server host 892 hosts a process that providesinformation representing video data for presentation at display 814. Itis contemplated that the components of system 800 can be deployed invarious configurations within other computer systems, e.g., host 882 andserver 892.

At least some embodiments of the invention are related to the use ofcomputer system 800 for implementing some or all of the techniquesdescribed herein. According to one embodiment of the invention, thosetechniques are performed by computer system 800 in response to processor802 executing one or more sequences of one or more processorinstructions contained in memory 804. Such instructions, also calledcomputer instructions, software and program code, may be read intomemory 804 from another computer-readable medium such as storage device808 or network link 878. Execution of the sequences of instructionscontained in memory 804 causes processor 802 to perform one or more ofthe method steps described herein. In alternative embodiments, hardware,such as ASIC 820, may be used in place of or in combination withsoftware to implement the invention. Thus, embodiments of the inventionare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link 878 and other networks throughcommunications interface 870, carry information to and from computersystem 800. Computer system 800 can send and receive information,including program code, through the networks 880, 890 among others,through network link 878 and communications interface 870. In an exampleusing the Internet 890, a server host 892 transmits program code for aparticular application, requested by a message sent from computer 800,through Internet 890, ISP equipment 884, local network 880 andcommunications interface 870. The received code may be executed byprocessor 802 as it is received, or may be stored in memory 804 or instorage device 808 or any other non-volatile storage for laterexecution, or both. In this manner, computer system 800 may obtainapplication program code in the form of signals on a carrier wave.

Various forms of computer readable media may be involved in carrying oneor more sequence of instructions or data or both to processor 802 forexecution. For example, instructions and data may initially be carriedon a magnetic disk of a remote computer such as host 882. The remotecomputer loads the instructions and data into its dynamic memory andsends the instructions and data over a telephone line using a modem. Amodem local to the computer system 800 receives the instructions anddata on a telephone line and uses an infra-red transmitter to convertthe instructions and data to a signal on an infra-red carrier waveserving as the network link 878. An infrared detector serving ascommunications interface 870 receives the instructions and data carriedin the infrared signal and places information representing theinstructions and data onto bus 810. Bus 810 carries the information tomemory 804 from which processor 802 retrieves and executes theinstructions using some of the data sent with the instructions. Theinstructions and data received in memory 804 may optionally be stored onstorage device 808, either before or after execution by the processor802.

FIG. 9 illustrates a chip set or chip 900 upon which an embodiment ofthe invention may be implemented. Chip set 900 is programmed to classifynatural language input into plurality of query processing levels asdescribed herein and includes, for instance, the processor and memorycomponents described with respect to FIG. 8 incorporated in one or morephysical packages (e.g., chips). By way of example, a physical packageincludes an arrangement of one or more materials, components, and/orwires on a structural assembly (e.g., a baseboard) to provide one ormore characteristics such as physical strength, conservation of size,and/or limitation of electrical interaction. It is contemplated that incertain embodiments the chip set 900 can be implemented in a singlechip. It is further contemplated that in certain embodiments the chipset or chip 900 can be implemented as a single “system on a chip.” It isfurther contemplated that in certain embodiments a separate ASIC wouldnot be used, for example, and that all relevant functions as disclosedherein would be performed by a processor or processors. Chip set or chip900, or a portion thereof, constitutes a means for performing one ormore steps of providing user interface navigation information associatedwith the availability of functions. Chip set or chip 900, or a portionthereof, constitutes a means for performing one or more steps ofclassifying natural language input into plurality of query processinglevels.

In one embodiment, the chip set or chip 900 includes a communicationmechanism such as a bus 901 for passing information among the componentsof the chip set 900. A processor 903 has connectivity to the bus 901 toexecute instructions and process information stored in, for example, amemory 905. The processor 903 may include one or more processing coreswith each core configured to perform independently. A multi-coreprocessor enables multiprocessing within a single physical package.Examples of a multi-core processor include two, four, eight, or greaternumbers of processing cores. Alternatively or in addition, the processor903 may include one or more microprocessors configured in tandem via thebus 901 to enable independent execution of instructions, pipelining, andmultithreading. The processor 903 may also be accompanied with one ormore specialized components to perform certain processing functions andtasks such as one or more digital signal processors (DSP) 907, or one ormore application-specific integrated circuits (ASIC) 909. A DSP 907typically is configured to process real-world signals (e.g., sound) inreal time independently of the processor 903. Similarly, an ASIC 909 canbe configured to performed specialized functions not easily performed bya more general purpose processor. Other specialized components to aid inperforming the inventive functions described herein may include one ormore field programmable gate arrays (FPGA), one or more controllers, orone or more other special-purpose computer chips.

In one embodiment, the chip set or chip 900 includes merely one or moreprocessors and some software and/or firmware supporting and/or relatingto and/or for the one or more processors.

The processor 903 and accompanying components have connectivity to thememory 905 via the bus 901. The memory 905 includes both dynamic memory(e.g., RAM, magnetic disk, writable optical disk, etc.) and staticmemory (e.g., ROM, CD-ROM, etc.) for storing executable instructionsthat when executed perform the inventive steps described herein toclassify natural language input into plurality of query processinglevels. The memory 905 also stores the data associated with or generatedby the execution of the inventive steps.

FIG. 10 is a diagram of exemplary components of a mobile terminal (e.g.,handset) for communications, which is capable of operating in the systemof FIG. 1A, according to one embodiment. In some embodiments, mobileterminal 1001, or a portion thereof, constitutes a means for performingone or more steps of classifying natural language input into pluralityof query processing levels. Generally, a radio receiver is often definedin terms of front-end and back-end characteristics. The front-end of thereceiver encompasses all of the Radio Frequency (RF) circuitry whereasthe back-end encompasses all of the base-band processing circuitry. Asused in this application, the term “circuitry” refers to both: (1)hardware-only implementations (such as implementations in only analogand/or digital circuitry), and (2) to combinations of circuitry andsoftware (and/or firmware) (such as, if applicable to the particularcontext, to a combination of processor(s), including digital signalprocessor(s), software, and memory(ies) that work together to cause anapparatus, such as a mobile phone or server, to perform variousfunctions). This definition of “circuitry” applies to all uses of thisterm in this application, including in any claims. As a further example,as used in this application and if applicable to the particular context,the term “circuitry” would also cover an implementation of merely aprocessor (or multiple processors) and its (or their) accompanyingsoftware/or firmware. The term “circuitry” would also cover ifapplicable to the particular context, for example, a baseband integratedcircuit or applications processor integrated circuit in a mobile phoneor a similar integrated circuit in a cellular network device or othernetwork devices.

Pertinent internal components of the telephone include a Main ControlUnit (MCU) 1003, a Digital Signal Processor (DSP) 1005, and areceiver/transmitter unit including a microphone gain control unit and aspeaker gain control unit. A main display unit 1007 provides a displayto the user in support of various applications and mobile terminalfunctions that perform or support the steps of classifying naturallanguage input into plurality of query processing levels. The display1007 includes display circuitry configured to display at least a portionof a user interface of the mobile terminal (e.g., mobile telephone).Additionally, the display 1007 and display circuitry are configured tofacilitate user control of at least some functions of the mobileterminal. An audio function circuitry 1009 includes a microphone 1011and microphone amplifier that amplifies the speech signal output fromthe microphone 1011. The amplified speech signal output from themicrophone 1011 is fed to a coder/decoder (CODEC) 1013.

A radio section 1015 amplifies power and converts frequency in order tocommunicate with a base station, which is included in a mobilecommunication system, via antenna 1017. The power amplifier (PA) 1019and the transmitter/modulation circuitry are operationally responsive tothe MCU 1003, with an output from the PA 1019 coupled to the duplexer1021 or circulator or antenna switch, as known in the art. The PA 1019also couples to a battery interface and power control unit 1020.

In use, a user of mobile terminal 1001 speaks into the microphone 1011and his or her voice along with any detected background noise isconverted into an analog voltage. The analog voltage is then convertedinto a digital signal through the Analog to Digital Converter (ADC)1023. The control unit 1003 routes the digital signal into the DSP 1005for processing therein, such as speech encoding, channel encoding,encrypting, and interleaving. In one embodiment, the processed voicesignals are encoded, by units not separately shown, using a cellulartransmission protocol such as enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., microwave access (WiMAX), LongTerm Evolution (LTE) networks, code division multiple access (CDMA),wideband code division multiple access (WCDMA), wireless fidelity(WiFi), satellite, and the like, or any combination thereof

The encoded signals are then routed to an equalizer 1025 forcompensation of any frequency-dependent impairments that occur duringtransmission though the air such as phase and amplitude distortion.After equalizing the bit stream, the modulator 1027 combines the signalwith a RF signal generated in the RF interface 1029. The modulator 1027generates a sine wave by way of frequency or phase modulation. In orderto prepare the signal for transmission, an up-converter 1031 combinesthe sine wave output from the modulator 1027 with another sine wavegenerated by a synthesizer 1033 to achieve the desired frequency oftransmission. The signal is then sent through a PA 1019 to increase thesignal to an appropriate power level. In practical systems, the PA 1019acts as a variable gain amplifier whose gain is controlled by the DSP1005 from information received from a network base station. The signalis then filtered within the duplexer 1021 and optionally sent to anantenna coupler 1035 to match impedances to provide maximum powertransfer. Finally, the signal is transmitted via antenna 1017 to a localbase station. An automatic gain control (AGC) can be supplied to controlthe gain of the final stages of the receiver. The signals may beforwarded from there to a remote telephone which may be another cellulartelephone, any other mobile phone or a land-line connected to a PublicSwitched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile terminal 1001 are received viaantenna 1017 and immediately amplified by a low noise amplifier (LNA)1037. A down-converter 1039 lowers the carrier frequency while thedemodulator 1041 strips away the RF leaving only a digital bit stream.The signal then goes through the equalizer 1025 and is processed by theDSP 1005. A Digital to Analog Converter (DAC) 1043 converts the signaland the resulting output is transmitted to the user through the speaker1045, all under control of a Main Control Unit (MCU) 1003 which can beimplemented as a Central Processing Unit (CPU).

The MCU 1003 receives various signals including input signals from thekeyboard 1047. The keyboard 1047 and/or the MCU 1003 in combination withother user input components (e.g., the microphone 1011) comprise a userinterface circuitry for managing user input. The MCU 1003 runs a userinterface software to facilitate user control of at least some functionsof the mobile terminal 1001 to classify natural language input intoplurality of query processing levels. The MCU 1003 also delivers adisplay command and a switch command to the display 1007 and to thespeech output switching controller, respectively. Further, the MCU 1003exchanges information with the DSP 1005 and can access an optionallyincorporated SIM card 1049 and a memory 1051. In addition, the MCU 1003executes various control functions required of the terminal. The DSP1005 may, depending upon the implementation, perform any of a variety ofconventional digital processing functions on the voice signals.Additionally, DSP 1005 determines the background noise level of thelocal environment from the signals detected by microphone 1011 and setsthe gain of microphone 1011 to a level selected to compensate for thenatural tendency of the user of the mobile terminal 1001.

The CODEC 1013 includes the ADC 1023 and DAC 1043. The memory 1051stores various data including call incoming tone data and is capable ofstoring other data including music data received via, e.g., the globalInternet. The software module could reside in RAM memory, flash memory,registers, or any other form of writable storage medium known in theart. The memory device 1051 may be, but not limited to, a single memory,CD, DVD, ROM, RAM, EEPROM, optical storage, magnetic disk storage, flashmemory storage, or any other non-volatile storage medium capable ofstoring digital data.

An optionally incorporated SIM card 1049 carries, for instance,important information, such as the cellular phone number, the carriersupplying service, subscription details, and security information. TheSIM card 1049 serves primarily to identify the mobile terminal 1001 on aradio network. The card 1049 also contains a memory for storing apersonal telephone number registry, text messages, and user specificmobile terminal settings.

Further, one or more camera sensors 1053 may be incorporated onto themobile station 1001 wherein the one or more camera sensors may be placedat one or more locations on the mobile station. Generally, the camerasensors may be utilized to capture, record, and cause to store one ormore still and/or moving images (e.g., videos, movies, etc.) which alsomay comprise audio recordings.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder.

1. A method comprising: parsing at least one natural language input intoone or more words, wherein the at least one natural language inputrepresents at least one cartographic query; mapping the one or morewords to at least one function of a cartography system; determining anavailability of the at least one function, one or more parametersassociated with the at least one function, or a combination thereof withrespect to at least one component of the cartography system; classifyingthe at least one cartographic query into at least one of a plurality ofquery processing levels based, at least in part, on the availability;initiating an execution of the at least one function at the classifiedat least one query processing level, wherein the execution of the atleast one function generates one or more results of the at least onecartographic query based, at least in part, on the one or moreparameters; and causing, at least in part, a presentation of the one ormore results as cartographic information in a user interface of thecartography system.
 2. A method of claim 1, wherein the plurality of thequery processing levels include, at least in part, a first processinglevel at which the at least one function, the one or more parameters, ora combination thereof are executed in whole locally at the at least onecomponent of the cartography system.
 3. A method of claim 2, wherein theplurality of the query processing levels include, at least in part, asecond processing level at which the at least one function, the one ormore parameters, or a combination thereof are executed, at least inpart, by conducting at least one search external to the at leastcomponent of the cartography system.
 4. A method of claim 3, wherein theplurality of the query processing levels include, at least in part, athird processing level at which the at least one function, the one ormore parameters, or a combination thereof are executed, at least inpart, by routing between at least two locations.
 5. A method of claim 4,further comprising: determining the at least two locations based, atleast in part, on the at least one function, the one or more parameters,contextual information, or a combination thereof.
 6. A method of claim4, further comprising: determining the routing between the at least twolocations without regard to an input order of at least one location. 7.A method of claim 1, further comprising: determining the one or moreparameters from the natural language input.
 8. A method of claim 1,further comprising: determining the one or more parameters from at leastone contextual cache, wherein the at least one contextual cache storesone or more previous results, one or more previous functions, one ormore previous parameters, or a combination thereof associated with atleast one previous cartographic query.
 9. A method of claim 1, whereinthe determining of the availability, the classifying of the at least onecartographic query, the execution of the at least one function, or acombination thereof is performed on receiving the at least one naturallanguage input by the at least one component of the cartography system.10. A method of claim 1, further comprising: formatting the one or moreresults according to at least one format associated with at least onemodule external to the at least one component; and transmitting theformatted one or more results to the at least one module to initiate thepresentation of the one or more results in the user interface.
 11. Anapparatus comprising: at least one processor; and at least one memoryincluding computer program code for one or more programs, the at leastone memory and the computer program code configured to, with the atleast one processor, cause the apparatus to perform at least thefollowing, parse at least one natural language input into one or morewords, wherein the at least one natural language input represents atleast one cartographic query; map the one or more words to at least onefunction of a cartography system; determine an availability of the atleast one function, one or more parameters associated with the at leastone function, or a combination thereof with respect to at least onecomponent of the cartography system; classify the at least onecartographic query into at least one of a plurality of query processinglevels based, at least in part, on the availability; initiate anexecution of the at least one function at the classified at least onequery processing level, wherein the execution of the at least onefunction generates one or more results of the at least one cartographicquery based, at least in part, on the one or more parameters; and cause,at least in part, a presentation of the one or more results ascartographic information in a user interface of the cartography system.12. An apparatus of claim 11, wherein the plurality of the queryprocessing levels include, at least in part, a first processing level atwhich the at least one function, the one or more parameters, or acombination thereof are executed in whole locally at the at least onecomponent of the cartography system.
 13. An apparatus of claim 12,wherein the plurality of the query processing levels include, at leastin part, a second processing level at which the at least one function,the one or more parameters, or a combination thereof are executed, atleast in part, by conducting at least one search external to the atleast component of the cartography system.
 14. An apparatus of claim 13,wherein the plurality of the query processing levels include, at leastin part, a third processing level at which the at least one function,the one or more parameters, or a combination thereof are executed, atleast in part, by routing between at least two locations.
 15. Anapparatus of claim 14, wherein the apparatus is further caused to:determine the at least two locations based, at least in part, on the atleast one function, the one or more parameters, contextual information,or a combination thereof.
 16. An apparatus of claim 14, wherein theapparatus is further caused to: determine the routing between the atleast two locations without regard to an input order of at least onelocation.
 17. An apparatus of claim 11, wherein the apparatus is furthercaused to: determine the one or more parameters from the naturallanguage input.
 18. A computer-readable storage medium carrying one ormore sequences of one or more instructions which, when executed by oneor more processors, cause an apparatus to at least perform the followingsteps: parsing at least one natural language input into one or morewords, wherein the at least one natural language input represents atleast one cartographic query; mapping the one or more words to at leastone function of a cartography system; determining an availability of theat least one function, one or more parameters associated with the atleast one function, or a combination thereof with respect to at leastone component of the cartography system; classifying the at least onecartographic query into at least one of a plurality of query processinglevels based, at least in part, on the availability; initiating anexecution of the at least one function at the classified at least onequery processing level, wherein the execution of the at least onefunction generates one or more results of the at least one cartographicquery based, at least in part, on the one or more parameters; andcausing, at least in part, a presentation of the one or more results ascartographic information in a user interface of the cartography system.19. A computer-readable storage medium of claim 18, wherein theplurality of the query processing levels include, at least in part, afirst processing level at which the at least one function, the one ormore parameters, or a combination thereof are executed in whole locallyat the at least one component of the cartography system.
 20. Acomputer-readable storage medium of claim 19, wherein the plurality ofthe query processing levels include, at least in part, a secondprocessing level at which the at least one function, the one or moreparameters, or a combination thereof are executed, at least in part, byconducting at least one search external to the at least component of thecartography system. 21.-48. (canceled)